CPIM-8.0 Exam Results

Time series decomposition is a method that breaks down a time series of historical demand data into its components: trend, seasonality, cyclical, and random. It is appropriate for forecasting the demand for a product family when there is a significant trend and seasonality in the demand history, as it can isolate and estimate these components and project them into the future. Time series decomposition can also handle cyclical and random variations in demand, and it can be applied to different time intervals (such as monthly, quarterly, or yearly). The other methods are not suitable for this scenario. Econometric models are complex mathematical models that use regression analysis to relate demand to various explanatory variables, such as price, income, or advertising. They are not designed to capture trend and seasonality in demand. Computer simulation is a technique that uses a computer program to mimic the behavior of a real system under different scenarios and assumptions. It is not a forecasting method per se, but rather a tool for testing and evaluating different forecasting methods or policies. Weighted moving average is a simple method that uses the average of the most recent observations as the forecast for the next period, with more weight given to the recent observations than the older ones. It is not able to capture trend and seasonality in demand, as it assumes that demand is stable and does not change over time. References: Time Series Decomposition | APICS Dictionary Term of the Day, APICS CPIM 8 Planning and Inventory Management | ASCM

Reducing the inventory value by 10% would contribute most to shortening the cash-to-cash cycle time. The cash-to-cash cycle time is calculated as the days of inventory outstanding plus days of sales outstanding minus days of payables outstanding. By reducing the inventory value, the company can decrease the days of inventory outstanding, leading to a shorter cash-to-cash cycle time. This aligns with CPIM’s focus on efficient inventory management to optimize the supply chain. References: The concepts are covered in detail in Module 4: Inventory Management